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How London became the first smart city back in 1854

 London became the first smart city back in 1854, argues Eddie Copeland, when it first used data to solve a civic problem by creating a life-saving map. But, he adds, the powers overseeing the capital still struggle to follow through on that legacy.

Those keen on London historical trivia may recognise the story that Copeland, the director of government innovation at future-looking quango Nesta, is referencing from the date alone. 1854 was the year of the Soho cholera outbreak, when Dr John Snow famously saved the day by figuring out that the disease was spread by water rather than “bad” air.

At the time, Copeland explained before a talk at Nesta’s FutureFest conference in London over the weekend, people thought disease came “from bad smells passed through the air. And poorer people who were less hygienic were [seen as] vulnerable due to their poor moral standing.”

As an anaesthetist who used gas to knock out his patients, Dr Snow realised that the digestive symptoms of cholera suggested it had nothing to do with lungs, as public health officials believed.

“So he started plotting the data,” Copeland says, “literally drawing on a map where people had died, and traced it down to the fact all those people lived in proximity to a certain water pump.”

Dr Snow didn’t just pinpoint the deadly water source: he spotted that a brewery within proximity of the infected pump saw no deaths, because the workers drank their product rather than local water.

Rather apt, then, that there’s a pub celebrating Snow in Soho. “It was fairly unprecedented. The use of statistics to diagnose and confirm a hypothesis was relatively new at that stage.”

Snow’s map. Image: public domain.

Still not very smart

As much as we all love a good map, is that alone enough to be called a “smart city”?

Copeland argues the term “smart city” is less about the technology – “the internet of things, driverless cars, talking lamp posts” – and more about using data to solve urban problems. “Those kind of data techniques basically inform the way cities are doing data analytics today – including projects by the mayor of London right now – and have barely changed at all.”

Indeed, Nesta is at this very moment running a similar project, plotting housing complaints on a map to find hot spots of unlicensed landlords overstuffing rental properties.

Such data isn’t always either welcome or understood – but that’s not new, and reflects another lesson we could have learned from Dr Snow’s experience in 1854. “They basically ignored him,” Copeland says of the health experts of the day, who instead cherry-picked facts to support their existing beliefs about smelly air.

Dr Snow’s data crunching did convince some local government leaders, who famously removed the Broad Street pump handle to prevent further outbreak. But the government wasn’t ready to “recognise it as a legitimate process”.

Fast forward to today. The idea we should collect and use data about citizens to inform city work – whether health care or infrastructure or transport or whatever else – is well established. But as Dr Snow learned, actual action can still depend on the existing beliefs of the local council.

“There’s a famous expression that we don’t so much have evidence-based policy making as policy-based evidence making,” Copeland says. Confirmation bias means we pick and choose data to fit our pre-existing ideas, just as the bad air theorists did in 1854.


Joined up jigsaw

London itself is “very forward thinking on open data”, Copeland said, with particular praise for the London DataStore. “But still, the GLA [Greater London Authority] does not collect any data sets from the London boroughs, other than stuff with a statutory obligation or planning applications.

“Even today,” he adds, “we have very few cases where we’re using data at a London scale – one exception would be TfL, which has a remit for transport across the capital, but it’s really rare.”

There are reasons for this. For a start, it’s not easy. Councils face technical challenges, with many using legacy systems that lock data into proprietary systems or hold it in different formats: location, for example, could be held as a grid reference, post code or full street address.

There are also legal barriers around data protection, real and perceived, and the usual cultural inertia, with some staff as yet unable to make the leap to sharing resources with neighbouring organisations.

And that doesn’t work, Copeland argues, if we’re to make leaps forward in our urban understanding as Dr Snow did. “Everyone has their piece of the jigsaw, no one can see the big picture,” he says. “And that’s what we’re trying to do, put the jigsaw together so we can start tackling these issues in a more intelligent way.”

Until we manage that, when it comes to data use in London, we still know nothing – sorry, John Snow.

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